RAPIDS cugraph_dgl enables the ability to use cugraph Property Graphs with DGL. This cugraph backend allows DGL users access to a collection of GPU-accelerated algorithms for graph analytics, such as sampling, centrality computation, and community detection.
The goal of cugraph_dgl is to enable Multi-Node Multi-GPU cugraph accelerated graphs to help train large-scale Graph Neural Networks(GNN) on DGL by providing a duck-typed version of the DGLGraph which uses cugraph for storing graph structure and node/edge feature data.
+from cugraph_dgl.convert import cugraph_storage_from_heterograph
+cugraph_g = cugraph_storage_from_heterograph(dgl_g)
sampler = dgl.dataloading.NeighborSampler(
[15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])
train_dataloader = dgl.dataloading.DataLoader(
- dgl_g,
+ cugraph_g,
train_idx,
sampler,
device=device,
batch_size=1024,
shuffle=True,
drop_last=False,
num_workers=0)